Journal of Water Resources Planning and Management

Peak shaving is a common challenge facing most provincial power grids (PPGs) in China. This paper presents an optimization model for the generation scheduling of multiple PPGs using peak-shaving indexes. Loads and power structures were considered to develop four indexes for quantifying the comprehensive peak-shaving needs of a PPG. The peak and off-peak capacity indexes, which represent the upward and downward balance pressures during hours of heavy and light loads, respectively, are functions of the dispatchable power, load extrema, and reserves. The rapid response index is defined to reflect the load-tracking pressure from off-peak to peak hours or vice versa. The fluctuation smoothing index represents the need for flexible regulation in response to frequent load changes in opposite directions. These indexes are normalized and integrated to establish a multiobjective model for peak operations in PPGs, subject to transmission energy and network limitations. This model is transformed into a solvable mixed-integer nonlinear programming model using polynomial and linear approximations and is solved with a branch-and-bound-based method. The model was implemented for the generation scheduling of the Xiluodu Hydropower Plant, which serves two PPGs. Case studies showed that the developed indexes provide accurate guidance for producing day-ahead generation schedules and distributing power among PPGs. The peak-shaving pressures are significantly alleviated in terms of index changes compared to an existing model.

Rainwater is regarded as an alternative water source for water supply augmentation in metropolitan areas to mitigate rising water stress caused by increasing population and climate change. There exists a knowledge gap in the evaluation of rainwater-harvesting systems to increase urban water supply reliability. This study provides insight into the impacts of developing rainwater-harvesting systems to improve urban water supply reliability by modeling joint water supplies from both decentralized rainwater-harvesting systems and centralized stream reservoirs. The model is then applied to Melbourne, Australia, using two rainfall regimes. The results show that enlarging roof areas and rainwater tanks, especially for new homes, can increase volumetric rainwater supply reliability at the household scale. This not only increases supply for households but also increases water supply reliability of stream reservoirs at the regional scale. An average roof area of $200{\mathrm{m}}^2$ and an average tank size of 8–10 KL can harvest enough rainwater in Melbourne to increase reservoir water supply reliability to 90% for current urban water demand during an extended drought. Using these results, efficient sizes for rainwater tanks are determined by analyzing the cost of enlarging a tank compared with its marginal benefit of increasing rainwater supply reliability. Furthermore, the impacts of climate variability and demand seasonality on system performance are explored using storage-reliability-yield curves for rainwater-harvesting systems. These results provide general insights regarding the feasibility of rainwater water supply augmentation constrained by tank size, roof area, rainfall regime, and demand seasonality. The proposed method for coupling a rainwater tank model at the household scale with a reservoir model at the regional scale is transferable to the design of regional rainwater-harvesting strategies in other cities.

Increasing population and water use, rising pollution of water resources, and climate change affect the quantity and quality of water resources. Reservoir operation is an important tool for water supply that can be optimized by simulation-optimization considering the impact of climate change on water quality. This study presents a simulation-optimization approach linking the CE-QUAL-W2 hydrodynamic model with the firefly algorithm $k$-nearest neighbor (FA-KNN) model to obtain optimal reservoir discharges to achieve water quality objectives under climate change conditions. The developed algorithm overcomes the computational burden of CE-QUAL-W2. The FA-KNN hybrid algorithm is employed to optimize the total dissolved solids (TDS) while achieving computational efficiently beyond what could be achieved with CE-QUAL-W2 simulations alone. This paper’s approach is evaluated with the Aidoghmoush Reservoir (East Azerbaijan, Iran). Overall, 36 simulation-optimization scenarios for dry and wet years under baseline and climate change conditions are evaluated by considering three initial water levels for the reservoir (minimum, average, and normal) and three thresholds for assessing the hybrid algorithm. The TDS released from the reservoir in wet years would be acceptable for agricultural use; in dry years, on average, the TDS would not be acceptable for 24 days per year under climate change. The reservoir in winter undergoes complete mixing; it becomes stratified in spring and summer, and it is close to complete mixing in the autumn. The highest TDS in the reservoir would occur during the summer in dry years under climate change, reaching TDS of approximately $\mathrm{2,645}\mathrm{g}/{\mathrm{m}}^3$.

As populations and economies expand in regions with changing climates, demand for water can quickly grow beyond what natural supply can sustain. This paper proposes to mitigate shortages, such as those that occur along the Lower South Platte River in northeastern Colorado, via a three-step approach: (1) create flow data scenarios, which represent a wide array of hydroclimatic outcomes; (2) use an existing simulation model to process these scenarios in order to locate excess supply and unmet demand both spatially and temporally; and (3) minimize the cost of alleviating identified shortages using a new, multiperiod, two-stage stochastic programming model, which determines reservoir location, size, type, and operation over a 50-year time horizon. Results indicate that demand associated with historical and seasonally shifted scenarios can be satisfied, and unmet demand associated with more challenging reduced-mean runoff scenarios can be mitigated substantially. Furthermore, multiple smaller reservoirs are preferred over a single large reservoir to hedge against these uncertainties.

For California water resource planning in the face of climate change, hydrological and water distribution models require inputs of high spatial– and temporal–resolution temperature and precipitation projections. We used a quantile delta mapping (QDM) procedure along with bias correction and localized constructed analogs (LOCA) downscaling to produce 6-km temperature and precipitation fields that preserve the relative changes in these quantities from climate model projections. We developed a wetter moderate warming (WMW) case from the Representative Concentration Pathway (RCP) 4.5 emissions scenario and a dry extreme warming (DEW) case from the RCP8.5 scenario to establish a range of projected hydroclimatological conditions. In both cases, we found that extreme precipitation becomes more extreme, but the sign of changes in moderate precipitation events differs between the two cases. The precipitation estimate range is most broad in southern California, where it varies by a factor of 2 and is 50% across the Sierra Nevada. This approach, adopted by the California Department of Water Resources, balances a host of practical water resource planning considerations with the evolving state of the science for future hydroclimatological projections.

A Lagrangian method to simulate the advection, dispersion, and reaction of a single chemical, biological, or physical constituent within drinking water pipe networks is presented. This Lagrangian approach removes the need for fixed computational grids typically required in Eulerian and Eulerian-Lagrangian methods and allows for nonuniform computational segments. This makes the method fully compatible with the advection-reaction water quality engine currently used in EPANET. An operator splitting approach is used, in which the advection-reaction process is modeled before the dispersion process for each water quality step. The dispersion equation is discretized using a segment-centered finite-difference scheme, and flux continuity boundary conditions are applied at network junctions. A staged approach is implemented to solve the dispersion equation for interconnected pipe networks. First, a linear relationship between the boundary and internal concentrations is established for every pipe. Second, a symmetric and positive definite linear system of equations is constructed to calculate the concentrations at network junctions. Last, pipe internal concentrations are updated based on the junction concentrations. The solution generates exact results when the analytical solutions are available and leads to more accurate water quality simulations than advection-reaction-only water quality models, especially in the areas where dispersion dominates advection.

Water resource problems and management are complex, confusing, and controversial for participants in technical, policy, and public water deliberations. A thoughtful planning approach can reduce confusion and structure controversies. This paper attempts to summarize and organize various technical approaches to water resources planning. This paper summarizes the basic approach of rational planning, followed by brief reviews of requirements-based, benefit-cost-based, multiobjective, conflict resolution, market-based, and muddling through approaches to planning. Each approach has particular advantages and disadvantages for specific situations. Each approach also has somewhat different policy expectations and analytical requirements. These approaches are discussed in terms of practical contributions to addressing water problems in contemporary contexts, particularly for messy long-term regional water issues.

With the increasing adoption of advanced meter infrastructure (AMI), smarter sensors, and temporary and/or permanent data loggers, it is imperative to leverage data analytics methods with hydraulic modeling to improve the quality and efficiency of water service. One important task is to timely detect and evaluate anomaly events so that corresponding actions can be taken to prevent and mitigate the impact of possible water service disruption, which may be caused by the anomaly incidents including but not limited to pipe bursts and unauthorized water usages. In this paper, a comprehensive analysis framework is developed for anomaly event detection and evaluation by developing an integrated solution, which is implemented in multiple components including: (1) data-preprocess or cleansing to eliminate and correct error data records; (2) decomposition of time series data to ensure data stationarity; (3) outlier detection by statistical process control methods with stationary time series; (4) classification of system anomaly events by either correlation analysis of high-flow events with low-pressure events or high-flow outliers with low-pressure outliers; and (5) quantitative evaluation of the system anomaly events with field reported leak incidents. The solution framework has been applied to the water supply zone that is permanent monitored with the flow meter at the inlet and 12 pressure stations throughout the zone with more than 8,000 pipes. Analysis has been conducted with one-year monitoring data and 106 historical leak records, which are employed to validate 526 detected anomaly events. Among them, a 75% true positive rate has been achieved and 90% of 106 field events have been successfully detected with a lead time of more than 24 h. The results obtained indicate that the developed solution method is effective at facilitating the operational management of a smart water grid by maximizing the return of investment in continuously monitoring water distribution networks.

Accurate calculation of power generation output is crucial to the operation and management of reservoir. The calculation of power generation output is related to the downstream water level, which usually is obtained by interpolation of discharge flow. However, the interpolation method has a large error and adversely affects the output calculation, especially for medium and low water head reservoirs. This study explored the relevant factors of the downstream water level and accurately predicted it from historical operational data. The maximal information coefficient and feature combination were used to select feature inputs, and a deep neural network was designed based on a convolutional neural network and a long short-term memory network to predict the downstream water level of a reservoir. To verify the performance of designed model, it was compared with the interpolation method and 4 state-of-the-art prediction methods using 12 validation sets of Gezhouba Reservoir. The experimental results showed that downstream water level obtained by the designed model was closer to the actual water level than was the interpolated water level. Compared with four state-of-the-art prediction methods, the designed method also was very competitive. Finally, the influence of CNNLSTM on power generation output is compared with traditional interpolation method. The comparison results showed that the convolutional neural network–long short-term memory network method reduced the influence of the interpolation method by 92.74% on average.

Water markets represent a favorable means to reallocate water resources. With global changes in water consumption patterns and caps, water rights trading has become an appropriate method to reallocate water between agriculture and industry in water-rich regions. This paper analyzed the economic benefits of water trading in water-rich regions, with reference to a case study of southern China. Based on a regional input–output table, the economic benefits that water trading brings to the regional economy were quantified, including related economic benefits, indirect economic benefits, and cumulative economic benefits. The results showed that water trading yields significant economic benefits for the regional economy because of the accompanying technological and economic connections established among different sectors. This study provides a reference point for water rights trading policies in water-rich regions.